Enhance sales opportunities at physical commerce channels

Abstract

A method, computer system, and a computer program product for enhancing a customer experience is provided. The present invention may include collecting data for a store visit. The present invention may include determining whether to offer the user one or more rewards based on the data collected. The present invention may include determining the one or more rewards to offer the user. The present invention may include presenting the one or more rewards to the user.

1 . A method for enhancing a customer experience, the method comprising:
 collecting data for a store visit;   determining whether to offer a user one or more rewards based on the data collected and one or more thresholds corresponding to one or more user activities;   determining the one or more rewards to offer the user, wherein the one or more rewards are determined utilizing a machine learning model based on exceeding at least one of the one or more thresholds and an analysis of the data collected for the store visit;   presenting the one or more rewards to the user on an end user device, wherein the one or more rewards are ranked within a user interface based on an intent of the user's store visit, and wherein the user selects at least one of the one or more rewards in the user interface; and   receiving feedback from the user with respect to the one or more rewards, in response to one or more prompts displayed on the end user device, wherein the feedback is stored in a knowledge corpus and utilized in improving future reward offer determinations by the machine learning model.   
     
     
         2 . The method of  claim 1 , further comprising:
 adjusting the one or more thresholds corresponding to the one or more user activities based on a quality or quantity of the feedback received.   
     
     
         3 . The method of  claim 1 , further comprising:
 providing assistance to the user during the store visit, wherein the assistance includes a personalized map from item to item of a shopping list based upon a Global Positioning Systems (GPS) data received from a smart wearable device associated with the user.   
     
     
         4 . The method of  claim 1 , wherein collecting data for the store visit further comprises:
 utilizing at least data provided by the user prior to the store visit and during the store visit, wherein the data provided by the user prior to the store visit includes data collected from one or more Internet of Things (IoT) devices associated with a user profile, wherein the one or more IoT devices associated with the user profile are utilized in monitoring consumption patterns and product preferences of the user, and wherein the data provided during the store visit includes one or more responses to one or more prompts displayed to the user on the end user device and product information for one or more products for which the user is interacting with during the store visit, wherein the product information is retrieved from a catalog of products maintained in the knowledge corpus for a store associated with the store visit based on an analysis of video feed received from the end user device using one or more image analysis tools, wherein the one or more image analysis tools utilize at least object segmentation techniques and pre-trained classifiers.   
     
     
         5 . (canceled) 
     
     
         6 . The method of  claim 1 , wherein the one or more rewards presented to the user in the user interface on the end user device are updated intermittently as additional thresholds are exceeded. 
     
     
         7 . (canceled) 
     
     
         8 . A computer system for enhancing a customer experience, comprising:
 one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:   collecting data for a store visit;   determining whether to offer a user one or more rewards based on the data collected and one or more thresholds corresponding to one or more user activities;   determining the one or more rewards to offer the user, wherein the one or more rewards are determined utilizing a machine learning model based on exceeding at least one of the one or more thresholds and an analysis of the data collected for the store visit;   presenting the one or more rewards to the user on an end user device, wherein the one or more rewards are ranked within a user interface based on an intent of the user's store visit, and wherein the user selects at least one of the one or more rewards in the user interface; and   receiving feedback from the user with respect to the one or more rewards, in response to one or more prompts displayed on the end user device, wherein the feedback is stored in a knowledge corpus and utilized in improving future reward offer determinations by the machine learning model.   
     
     
         9 . The computer system of  claim 8 , further comprising:
 program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to adjust the one or more thresholds corresponding to the one or more user activities based on a quality or quantity of the feedback received.   
     
     
         10 . The computer system of  claim 8 , further comprising:
 program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to provide assistance to the user during the store visit, wherein the assistance includes a personalized map from item to item of a shopping list based upon a Global Positioning Systems (GPS) data received from a smart wearable device associated with the user.   
     
     
         11 . The computer system of  claim 8 , wherein collecting data for the store visit further comprises:
 program instructions, stored on at least one of the one or more computer-readable storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to utilize at least data provided by the user prior to the store visit and during the store visit, wherein the data provided by the user prior to the store visit includes data collected from one or more Internet of Things (IoT) devices associated with a user profile, wherein the one or more IoT devices associated with the user profile are utilized in monitoring consumption patterns and product preferences of the user, and wherein the data provided during the store visit includes one or more responses to one or more prompts displayed to the user on the end user device and product information for one or more products for which the user is interacting with during the store visit, wherein the product information is retrieved from a catalog of products maintained in the knowledge corpus for a store associated with the store visit based on an analysis of video feed received from the end user device using one or more image analysis tools, wherein the one or more image analysis tools utilize at least object segmentation techniques and pre-trained classifiers.   
     
     
         12 . (canceled) 
     
     
         13 . The computer system of  claim 8 , wherein the one or more rewards presented to the user in the user interface on the end user device are updated intermittently as additional thresholds are exceeded. 
     
     
         14 . (canceled) 
     
     
         15 . A computer program product for enhancing a customer experience, comprising:
 one or more non-transitory computer-readable storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor to cause the processor to perform a method comprising:   collecting data for a store visit;   determining whether to offer a user one or more rewards based on the data collected and one or more thresholds corresponding to one or more user activities;   determining the one or more rewards to offer the user, wherein the one or more rewards are determined utilizing a machine learning model based on exceeding at least one of the one or more thresholds and an analysis of the data collected for the store visit;   presenting the one or more rewards to the user on an end user device, wherein the one or more rewards are ranked within a user interface based on an intent of the user's store visit, and wherein the user selects at least one of the one or more rewards in the user interface; and   receiving feedback from the user with respect to the one or more rewards, in response to one or more prompts displayed on the end user device, wherein the feedback is stored in a knowledge corpus and utilized in improving future reward offer determinations by the machine learning model.   
     
     
         16 . The computer program product of  claim 15 , further comprising:
 program instructions, stored on at least one of the one or more computer-readable storage media, to adjust the one or more thresholds corresponding to the one or more user activities based on a quality or quantity of the feedback received.   
     
     
         17 . The computer program product of  claim 15 , further comprising:
 program instructions, stored on at least one of the one or more computer-readable storage media, to provide assistance to the user during the store visit, wherein the assistance includes a personalized map from item to item of a shopping list based upon a Global Positioning Systems (GPS) data received from a smart wearable device associated with the user.   
     
     
         18 . The computer program product of  claim 15 , wherein collecting data for the store visit further comprises:
 program instructions, stored on at least one of the one or more computer-readable storage media, to utilize at least data provided by the user prior to the store visit and during the store visit, wherein the data provided by the user prior to the store visit includes data collected from one or more Internet of Things (IoT) devices associated with a user profile, wherein the one or more IoT devices associated with the user profile are utilized in monitoring consumption patterns and product preferences of the user, and wherein the data provided during the store visit includes one or more responses to one or more prompts displayed to the user on the end user device and product information for one or more products for which the user is interacting with during the store visit, wherein the product information is retrieved from a catalog of products maintained in the knowledge corpus for a store associated with the store visit based on an analysis of video feed received from the end user device using one or more image analysis tools, wherein the one or more image analysis tools utilize at least object segmentation techniques and pre-trained classifiers.   
     
     
         19 . (canceled) 
     
     
         20 . The computer program product of  claim 15 , wherein the one or more rewards presented to the user in the user interface on the end user device are updated intermittently as additional thresholds are exceeded. 
     
     
         21 . The method of  claim 1 , wherein the intent of the user's store visit is determined based on the one or more thresholds exceeded by the user and the analysis of the data collected for the store visit using one or more linguistic analysis techniques, wherein the one or more linguistic analysis techniques includes at least Natural Language Processing. 
     
     
         22 . (canceled) 
     
     
         23 . The computer system of  claim 8 , wherein the intent of the user's store visit is determined based on the one or more thresholds exceeded by the user and the analysis of the data collected for the store visit using one or more linguistic analysis techniques, wherein the one or more linguistic analysis techniques includes at least Natural Language Processing. 
     
     
         24 . (canceled) 
     
     
         25 . The computer program product of  claim 15 , wherein the intent of the user's store visit is determined based on the one or more thresholds exceeded by the user and the analysis of the data collected for the store visit using one or more linguistic analysis techniques, wherein the one or more linguistic analysis techniques includes at least Natural Language Processing. 
     
     
         26 . The method of  claim 1 , wherein the machine learning model utilizes two or more machine learning algorithms, wherein the two or more machine learning algorithms are specific to a type of data collected for the store visit, and wherein the feedback received from the user is utilized as additional input for a corresponding machine learning algorithm. 
     
     
         27 . The method of  claim 1 , wherein the data collected for the store visit includes at least consumption patterns derived from one or more smart appliances, and wherein at least one of the one or more thresholds are derived from a professional recommendation made through an integrated medicine-as-a-service practice.